class_docstr: 3-dimensional convolution layer.
  
  .. note::
  
      This link wraps :class:`~chainer.links.ConvolutionND` by giving 3 to
      the first argument ``ndim``, so see the details of the behavior in
      the documentation of :class:`~chainer.links.ConvolutionND`.
class_docstr_tok: ["3-dimensional","convolution","layer",".","..","note",":",":","This","link","wraps",":","class",":","`","~chainer.links.ConvolutionND","`","by","giving","3","to","the","first","argument","``","ndim","``",",","so","see","the","details","of","the","behavior","in","the","documentation","of",":","class",":","`","~chainer.links.ConvolutionND","`","."]
code: def __init__(
      self,
      in_channels,
      out_channels,
      ksize,
      stride=1,
      pad=0,
      nobias=False,
      initialW=None,
      initial_bias=None,
      cover_all=False,
      dilate=1,
      groups=1,
  ):
      super(Convolution3D, self).__init__(
          3,
          in_channels,
          out_channels,
          ksize,
          stride,
          pad,
          nobias,
          initialW,
          initial_bias,
          cover_all,
          dilate,
          groups,
      )
  
file_path: chainer/chainer/links/connection/convolution_nd.py
imports: from chainer.functions.connection import convolution_nd
  from chainer import initializers
  from chainer import link
  from chainer.utils import argument
  from chainer.utils import conv_nd
  from chainer import variable
label_desc: Use image node_id 1 to create a new Convolution3D object from inherited base classes: ConvolutionND with example: obj = Convolution3D(in_channels, out_channels, ksize, stride, pad, nobias, initialW, initial_bias, cover_all, dilate, groups)